Feasibility Control of Constrained Biological Regulation: Code and Data

This repository contains the computational code, parameter sets, and datasets supporting the study:“A Feasibility Control Law for Constrained Biological Regulation.” The work introduces a reduced dynamical framework in which a broad class of biological regulatory systems can be represented using a dimensionless feasibility functional Φ=Ξ(1−Ψ)1+Λ,\Phi = \frac{\Xi (1 - \Psi)}{1 + \Lambda},Φ=1+ΛΞ(1−Ψ), where Ξ\XiΞ denotes effective regulatory gain, Λ\LambdaΛ burden throughput, and Ψ\PsiΨ saturation of corrective capacity. The condition Φ<1\Phi < 1Φ<1 defines a feasibility boundary governing the accessibility of stable regulated equilibria. The repository includes numerical simulations, parameter sweeps, and figure-generation scripts demonstrating how systems as diverse as tumor–immune regulation, proteostasis dynamics, ecological feedback systems, and synthetic gene circuits can be mapped onto a common reduced feasibility space. All simulations were implemented in Python (SciPy) and MATLAB. The repository is structured to enable full reproducibility of the results and figures presented in the manuscript, with all scripts executable without modification using the provided datasets and parameter definitions. The framework applies to systems in which regulatory dynamics are constrained by finite control capacity, transport limitations, and saturation effects, and demonstrates that regulatory failure emerges as a constraint-driven dynamical transition rather than a gradual loss of function. This Zenodo record provides a permanent, citable archive of the code and data used in the study.